In these two tables, “Grad-CAM (no norm)" means Grad-CAM with gradient computed from the product of two embedding vectors without normalization and “Decomposition+Bias" denotes the first two terms of Eq. REF . Since the architecture of [1]} is based on GMP, “Grad-CAM (no norm)" is not equivalent to “Decomposition+Bias" as shown in Section . The proposed framework outperforms Grad-CAM for metric learning as well as CAM based on classification. As justified in Section , computing the gradients before normalization does improve the performance of Grad-CAM by a large margin. The qualitative result in Fig. REF  also illustrates the scattering effect of Grad-CAM with normalization as demonstrated in our theoretical analysis (Appendix ).
